AUTHOR=Meng Chaolu , Hu Yang , Zhang Ying , Guo Fei 

TITLE=PSBP-SVM: A Machine Learning-Based Computational Identifier for Predicting Polystyrene Binding Peptides

JOURNAL=Frontiers in Bioengineering and Biotechnology

VOLUME=8

YEAR=2020

URL=https://www.frontiersin.org/journals/bioengineering-and-biotechnology/articles/10.3389/fbioe.2020.00245

DOI=10.3389/fbioe.2020.00245

ISSN=2296-4185

ABSTRACT=<p>Polystyrene binding peptides (PSBPs) play a key role in the immobilization process. The correct identification of PSBPs is the first step of all related works. In this paper, we proposed a novel support vector machine-based bioinformatic identification model. This model contains four machine learning steps, including feature extraction, feature selection, model training and optimization. In a five-fold cross validation test, this model achieves 90.38, 84.62, 87.50, and 0.90% SN, SP, ACC, and AUC, respectively. The performance of this model outperforms the state-of-the-art identifier in terms of the SN and ACC with a smaller feature set. Furthermore, we constructed a web server that includes the proposed model, which is freely accessible at <ext-link ext-link-type="uri" xlink:href="http://server.malab.cn/PSBP-SVM/index.jsp" xmlns:xlink="http://www.w3.org/1999/xlink">http://server.malab.cn/PSBP-SVM/index.jsp</ext-link>.</p>